Outline

1) Project Aims

Aim 1: Predict degree of improvement in ruminative and depressive symptoms: RRS & HDRS-6.

Aim 2: Determine which treatment-specific models predict across treatment arms.

2) Sample Outline

group n mean_age sd_age n_males
e 26 40.00000 13.74336 12
k 49 38.85714 10.79159 25
s 52 32.48077 11.22408 23

  Df Sum Sq Mean Sq F value Pr(>F)
group 2 2.583 1.292 11.13 1.876e-05
**scale**       3    7.736     2.579     22.23    1.694e-13 

group:scale 6 1.757 0.2928 2.524 0.02047

Residuals 483 56.03 0.116 NA NA

Table: 2-way ANOVA Table: Treatment-by-Scale Changes

Quitting from lines 51-85 (NARSAD_Aim_1_Summary.Rmd) Error in pander(ph\(`group:scale`[ph\)group:scale[, “p adj”] < 0.05, ], : object ‘ph’ not found Calls: … withCallingHandlers -> withVisible -> eval -> eval -> pander In addition: Warning messages: 1: Removed 13 rows containing non-finite values (stat_boxplot). 2: Removed 13 rows containing non-finite values (stat_boxplot).

3) Model Selection

Goal:Train and test several types of classifiers (Random Forests, Radial SVM, Gradient Boosted Trees) to narrow down which are best

3a) Coarse search & hyperparameters

Performance by classifier and use of baseline symptoms

Result: Gradient boosted trees consistently outperform both random forests and radial SVMs

Performance by classifier and correlation threshold hyperparameter

Result: A correlation threshold between 0.1 and 0.3 looks best. Now a more refined grid search using boosted trees and a strict cutoff can be done.

4) ECT Models, No Baseline Symptoms

4a) RRS: Reflection

## TableGrob (2 x 3) "arrange": 6 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 4 4 (2-2,1-1) arrange gtable[layout]
## 5 5 (2-2,2-2) arrange gtable[layout]
## 6 6 (2-2,3-3) arrange gtable[layout]

Frequently selected ICA components

4b) RRS: Brooding

## TableGrob (2 x 3) "arrange": 6 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 4 4 (2-2,1-1) arrange gtable[layout]
## 5 5 (2-2,2-2) arrange gtable[layout]
## 6 6 (2-2,3-3) arrange gtable[layout]

Frequently selected ICA components

4c) Rumination: TCQR

## TableGrob (2 x 3) "arrange": 6 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 4 4 (2-2,1-1) arrange gtable[layout]
## 5 5 (2-2,2-2) arrange gtable[layout]
## 6 6 (2-2,3-3) arrange gtable[layout]

Frequently selected ICA components

4d) HDRS 6-item scale

## TableGrob (2 x 3) "arrange": 6 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 4 4 (2-2,1-1) arrange gtable[layout]
## 5 5 (2-2,2-2) arrange gtable[layout]
## 6 6 (2-2,3-3) arrange gtable[layout]

Frequently selected ICA components

5) Ketamine Models, No Baseline Symptoms

5a) RRS: Reflection

## TableGrob (2 x 3) "arrange": 6 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 4 4 (2-2,1-1) arrange gtable[layout]
## 5 5 (2-2,2-2) arrange gtable[layout]
## 6 6 (2-2,3-3) arrange gtable[layout]

Frequently selected ICA components

5b) RRS: Brooding

## TableGrob (2 x 3) "arrange": 6 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 4 4 (2-2,1-1) arrange gtable[layout]
## 5 5 (2-2,2-2) arrange gtable[layout]
## 6 6 (2-2,3-3) arrange gtable[layout]

Frequently selected ICA components

5c) RRS: TCRQ

PvAc for this model was < 0 so not worth outlining results

5d) HDRS 6-item Scale

PvAc for this model very low, < 0.1, so not worth outlining results.

6) Sleep Deprivation Models, No Baseline Symptoms

6a) RRS: Reflection

## TableGrob (2 x 3) "arrange": 6 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 4 4 (2-2,1-1) arrange gtable[layout]
## 5 5 (2-2,2-2) arrange gtable[layout]
## 6 6 (2-2,3-3) arrange gtable[layout]

Frequently selected ICA components

6b) RRS: Brooding

## TableGrob (2 x 3) "arrange": 6 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 4 4 (2-2,1-1) arrange gtable[layout]
## 5 5 (2-2,2-2) arrange gtable[layout]
## 6 6 (2-2,3-3) arrange gtable[layout]

Frequently selected ICA components

6c) RRS: TCQR

## TableGrob (2 x 3) "arrange": 6 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 4 4 (2-2,1-1) arrange gtable[layout]
## 5 5 (2-2,2-2) arrange gtable[layout]
## 6 6 (2-2,3-3) arrange gtable[layout]

Frequently selected ICA components

6d) HDRS 6-item Scale

PvAc < 0; results not worth outlining

7) Performance of Models Across Treatment Arms

Coarse grid-search results by classifier

Result: Gradient boosted trees seem to generalize slightly better, on average.